The document discusses PageRank and its use in ranking the importance of web pages, including the concept of modeling web surfing as a random walk and calculating page importance based on the probability of ending up at a page. It also describes techniques for addressing issues like dead ends and spider traps in the web graph structure as well as methods for computing topic-specific PageRanks based on teleporting to relevant pages for a given topic.
The document discusses the importance of teacher web pages for saving classroom time, providing resources and access to students, encouraging technology use, improving communication, and allowing teacher creativity. Teacher web pages allow instructors to post notes, links, and assignments for students to access during and after class. This saves classroom time otherwise spent on distribution and allows students to complete work at home. Web pages also provide a space to communicate updates and expectations to students, parents, and the community.
Need and importance of website, educational CD's& learning Moduleanoop kp
1. Websites, educational CDs, and learning modules are important tools for online learning. Websites allow educational institutions to provide information to current and prospective students. Educational CDs allow students to interact with course content at their own pace. Learning modules package educational content in a logical sequence and allow instructors to structure required materials. These tools enhance learning by making information more accessible and interactive.
This document discusses the PageRank algorithm for ranking nodes in a graph based on their importance. It begins by introducing graph data examples like social networks and the web graph. It then describes how PageRank works by modeling a random walk over the graph and defining the stationary distribution of this random walk as the rank of each node. Key aspects covered include: using the eigenvector formulation to solve the system of equations efficiently via power iteration; adding random teleports to address problems of dead ends and spider traps; and formulating the full PageRank algorithm using a sparse matrix to handle large graphs. The document provides detailed explanations of the mathematical foundations and implementation of PageRank.
This document discusses the PageRank algorithm for ranking nodes in a graph based on link structure. It begins by introducing graph data examples like social networks and the web graph. It then presents the concept of links as votes, and formulates PageRank through a flow model and matrix formulation. It addresses problems with dead ends and spider traps in the graph and how the solution of random teleports resolves these. The complete PageRank algorithm involves iteratively computing the rank vector through matrix multiplication until convergence, while handling sparsity through a teleportation term in the Google matrix formulation.
This document discusses PageRank, an algorithm used by Google Search to rank websites in their search results. It describes how PageRank works by modeling the web as a directed graph and calculating an importance score for each page based on the page's inlinks. It discusses how PageRank can be formulated as the principal eigenvector of the stochastic link matrix or as the stationary distribution of a random walk on the web graph. It also covers techniques like random teleportation to address issues like spider traps and dead ends.
PageRank is Google's algorithm for ranking web pages. It defines a page's importance as the number of important pages that link to it. PageRank is calculated through an iterative process where each page distributes its rank value evenly among the pages it links to. This helps rank pages and address issues like dead ends and spider traps that could accumulate all importance.
This document discusses link analysis and PageRank, an algorithm for identifying important nodes in large network graphs. It begins with an overview of graph data structures and the goal of identifying influential nodes. It then introduces PageRank, explaining its basic assumptions and showing examples of how it calculates node importance scores. The document discusses problems with the initial PageRank approach and how it was improved with the Complete PageRank algorithm. Finally, it briefly introduces Topic-sensitive PageRank, which aims to identify important nodes related to specific topics.
The document discusses the importance of teacher web pages for saving classroom time, providing resources and access to students, encouraging technology use, improving communication, and allowing teacher creativity. Teacher web pages allow instructors to post notes, links, and assignments for students to access during and after class. This saves classroom time otherwise spent on distribution and allows students to complete work at home. Web pages also provide a space to communicate updates and expectations to students, parents, and the community.
Need and importance of website, educational CD's& learning Moduleanoop kp
1. Websites, educational CDs, and learning modules are important tools for online learning. Websites allow educational institutions to provide information to current and prospective students. Educational CDs allow students to interact with course content at their own pace. Learning modules package educational content in a logical sequence and allow instructors to structure required materials. These tools enhance learning by making information more accessible and interactive.
This document discusses the PageRank algorithm for ranking nodes in a graph based on their importance. It begins by introducing graph data examples like social networks and the web graph. It then describes how PageRank works by modeling a random walk over the graph and defining the stationary distribution of this random walk as the rank of each node. Key aspects covered include: using the eigenvector formulation to solve the system of equations efficiently via power iteration; adding random teleports to address problems of dead ends and spider traps; and formulating the full PageRank algorithm using a sparse matrix to handle large graphs. The document provides detailed explanations of the mathematical foundations and implementation of PageRank.
This document discusses the PageRank algorithm for ranking nodes in a graph based on link structure. It begins by introducing graph data examples like social networks and the web graph. It then presents the concept of links as votes, and formulates PageRank through a flow model and matrix formulation. It addresses problems with dead ends and spider traps in the graph and how the solution of random teleports resolves these. The complete PageRank algorithm involves iteratively computing the rank vector through matrix multiplication until convergence, while handling sparsity through a teleportation term in the Google matrix formulation.
This document discusses PageRank, an algorithm used by Google Search to rank websites in their search results. It describes how PageRank works by modeling the web as a directed graph and calculating an importance score for each page based on the page's inlinks. It discusses how PageRank can be formulated as the principal eigenvector of the stochastic link matrix or as the stationary distribution of a random walk on the web graph. It also covers techniques like random teleportation to address issues like spider traps and dead ends.
PageRank is Google's algorithm for ranking web pages. It defines a page's importance as the number of important pages that link to it. PageRank is calculated through an iterative process where each page distributes its rank value evenly among the pages it links to. This helps rank pages and address issues like dead ends and spider traps that could accumulate all importance.
This document discusses link analysis and PageRank, an algorithm for identifying important nodes in large network graphs. It begins with an overview of graph data structures and the goal of identifying influential nodes. It then introduces PageRank, explaining its basic assumptions and showing examples of how it calculates node importance scores. The document discusses problems with the initial PageRank approach and how it was improved with the Complete PageRank algorithm. Finally, it briefly introduces Topic-sensitive PageRank, which aims to identify important nodes related to specific topics.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
AI Trends in Creative Operations 2024 by Artwork Flow.pdfmarketingartwork
Creative operations teams expect increased AI use in 2024. Currently, over half of tasks are not AI-enabled, but this is expected to decrease in the coming year. ChatGPT is the most popular AI tool currently. Business leaders are more actively exploring AI benefits than individual contributors. Most respondents do not believe AI will impact workforce size in 2024. However, some inhibitions still exist around AI accuracy and lack of understanding. Creatives primarily want to use AI to save time on mundane tasks and boost productivity.
Organizational culture includes values, norms, systems, symbols, language, assumptions, beliefs, and habits that influence employee behaviors and how people interpret those behaviors. It is important because culture can help or hinder a company's success. Some key aspects of Netflix's culture that help it achieve results include hiring smartly so every position has stars, focusing on attitude over just aptitude, and having a strict policy against peacocks, whiners, and jerks.
PEPSICO Presentation to CAGNY Conference Feb 2024Neil Kimberley
PepsiCo provided a safe harbor statement noting that any forward-looking statements are based on currently available information and are subject to risks and uncertainties. It also provided information on non-GAAP measures and directing readers to its website for disclosure and reconciliation. The document then discussed PepsiCo's business overview, including that it is a global beverage and convenient food company with iconic brands, $91 billion in net revenue in 2023, and nearly $14 billion in core operating profit. It operates through a divisional structure with a focus on local consumers.
Content Methodology: A Best Practices Report (Webinar)contently
This document provides an overview of content methodology best practices. It defines content methodology as establishing objectives, KPIs, and a culture of continuous learning and iteration. An effective methodology focuses on connecting with audiences, creating optimal content, and optimizing processes. It also discusses why a methodology is needed due to the competitive landscape, proliferation of channels, and opportunities for improvement. Components of an effective methodology include defining objectives and KPIs, audience analysis, identifying opportunities, and evaluating resources. The document concludes with recommendations around creating a content plan, testing and optimizing content over 90 days.
How to Prepare For a Successful Job Search for 2024Albert Qian
The document provides guidance on preparing a job search for 2024. It discusses the state of the job market, focusing on growth in AI and healthcare but also continued layoffs. It recommends figuring out what you want to do by researching interests and skills, then conducting informational interviews. The job search should involve building a personal brand on LinkedIn, actively applying to jobs, tailoring resumes and interviews, maintaining job hunting as a habit, and continuing self-improvement. Once hired, the document advises setting new goals and keeping skills and networking active in case of future opportunities.
A report by thenetworkone and Kurio.
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See the original article on Forbes here:
http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9b
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Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
Diapo
1. Importance of Web Pages
PageRank
Topic-Specific PageRank
Hubs and Authorities
Combatting Link Spam
1
2. PageRank
Intuition: solve the recursive equation:
“a page is important if important pages
link to it.”
In high-falutin’ terms: importance =
the principal eigenvector of the
transition matrix of the Web.
A few fixups needed.
2
3. Transition Matrix of the Web
Enumerate pages.
Page i corresponds to row and column i.
M [i, j ] = 1/n if page j links to n
pages, including page i ; 0 if j does not
link to i.
M [i, j ] is the probability we’ll next be at
page i if we are now at page j.
3
5. Random Walks on the Web
Suppose v is a vector whose i th
component is the probability that each
random walker is at page i at a certain
time.
If each walker follows a link from i at
random, the probability distribution for
walkers is then given by the vector M v.
5
6. Random Walks – (2)
Starting from any vector v, the limit
M (M (…M (M v ) …)) is the long-term
distribution of walkers.
Intuition: pages are important in
proportion to how likely a walker is to
be there.
The math: limiting distribution =
principal eigenvector of M = PageRank.
6
7. Example: The Web in 1839
y a m
Yahoo y 1/2 1/2 0
a 1/2 0 1
m 0 1/2 0
Amazon M’soft
7
8. Solving The Equations
Because there are no constant terms,
the equations v = M v do not have a
unique solution.
In Web-sized examples, we cannot
solve by Gaussian elimination anyway;
we need to use relaxation (= iterative
solution).
Can work if you start with a fixed v.
8
9. Simulating a Random Walk
Start with the vector v = [1, 1,…, 1]
representing the idea that each Web
page is given one unit of importance.
Repeatedly apply the matrix M to v,
allowing the importance to flow like a
random walk.
About 50 iterations is sufficient to
estimate the limiting solution.
9
10. Example: Iterating Equations
Equations v = M v :
y = y /2 + a /2 Note: “=” is
a = y /2 + m really “assignment.”
m = a /2
y 1 1 5/4 9/8 6/5
a = 1 3/2 1 11/8 ... 6/5
m 1 1/2 3/4 1/2 3/5
10
16. Real-World Problems
Some pages are “dead ends” (have no
links out).
Such a page causes importance to leak out.
Other groups of pages are spider traps
(all out-links are within the group).
Eventually spider traps absorb all importance.
16
24. M’soft Becomes Spider Trap
y a m
Yahoo y 1/2 1/2 0
a 1/2 0 0
m 0 1/2 1
Amazon M’soft
24
25. Example: Effect of Spider Trap
Equations v = M v :
y = y /2 + a /2
a = y /2
m = a /2 + m
y 1 1 3/4 5/8 0
a = 1 1/2 1/2 3/8 ... 0
m 1 3/2 7/4 2 3
25
30. PageRank Solution to Traps, Etc.
“Tax” each page a fixed percentage at
each interation.
Add a fixed constant to all pages.
Models a random walk with a fixed
probability of leaving the system, and a
fixed number of new walkers injected
into the system at each step.
30
31. Example: Microsoft is a Spider
Trap; 20% Tax
Equations v = 0.8(M v ) + 0.2:
y = 0.8(y /2 + a/2) + 0.2
a = 0.8(y /2) + 0.2
m = 0.8(a /2 + m) + 0.2
y 1 1.00 0.84 0.776 7/11
a = 1 0.60 0.60 0.536 . . . 5/11
m 1 1.40 1.56 1.688 21/11
31
32. General Case
In this example, because there are no
dead-ends, the total importance
remains at 3.
In examples with dead-ends, some
importance leaks out, but total remains
finite.
32
33. Solving the Equations
Because there are constant terms, we
can expect to solve small examples by
Gaussian elimination.
Web-sized examples still need to be
solved by relaxation.
33
34. Finding a Good Starting Vector
1. Newton-like prediction of where
components of the principal
eigenvector are heading.
2. Take advantage of locality in the Web.
Each technique can reduce the
number of iterations by 50%.
Important – PageRank takes time!
34
35. Predicting Component Values
Three consecutive values for the
importance of a page suggests where
the limit might be.
1.0
Guess for the next round
0.7
0.6 0.55
35
36. Exploiting Substructure
Pages from particular domains, hosts,
or directories, like stanford.edu or
infolab.stanford.edu/~ullman
tend to have many internal links.
Initialize PageRank using ranks within
your local cluster, then ranking the
clusters themselves.
36
37. Strategy
Compute local PageRanks (in parallel?).
Use local weights to establish weights on
edges between clusters.
Compute PageRank on graph of clusters.
Initial rank of a page is the product of its
local rank and the rank of its cluster.
“Clusters” are appropriately sized regions
with common domain or lower-level detail.
37
38. In Pictures
1.5 2.05
3.0 2.0
0.15 0.1
Local ranks
Intercluster weights 0.05
Ranks of clusters
Initial eigenvector 38
39. Topic-Specific Page Rank
Goal: Evaluate Web pages not just
according to their popularity, but by
how close they are to a particular topic,
e.g. “sports” or “history.”
Allows search queries to be answered
based on interests of the user.
Example: Query Maccabi wants different
pages depending on whether you are
interested in sports or history.
39
40. Teleport Sets
Assume each walker has a small
probability of “teleporting” at any tick.
Teleport can go to:
1. Any page with equal probability.
As in the “taxation” scheme.
2. A topic-specific set of “relevant” pages
(teleport set ).
For topic-specific PageRank.
40
41. Example: Topic = Software
Only Microsoft is in the teleport set.
Assume 20% “tax.”
I.e., probability of a teleport is 20%.
41
42. Only Microsoft in Teleport Set
Yahoo
Dr. Who’s
phone
booth.
Amazon M’soft
42
49. Picking the Teleport Set
1. Choose the pages belonging to the
topic in Open Directory.
2. “Learn” from examples the typical
words in pages belonging to the topic;
use pages heavy in those words as the
teleport set.
49
50. Application: Link Spam
Spam farmers today create networks of
millions of pages designed to focus
PageRank on a few undeserving pages.
To minimize their influence, use a
teleport set consisting of trusted pages
only.
Example: home pages of universities.
50
51. Hubs and Authorities
Mutually recursive definition:
A hub links to many authorities;
An authority is linked to by many hubs.
Authorities turn out to be places where
information can be found.
Example: course home pages.
Hubs tell where the authorities are.
Example: CS Dept. course-listing page.
51
52. Transition Matrix A
H&A uses a matrix A [i, j ] = 1 if page i
links to page j, 0 if not.
AT, the transpose of A, is similar to the
PageRank matrix M, but AT has 1’s
where M has fractions.
52
54. Using Matrix A for H&A
Powers of A and AT have elements of
exponential size, so we need scale factors.
Let h and a be vectors measuring the
“hubbiness” and authority of each page.
Equations: h = λAa; a = µAT h.
Hubbiness = scaled sum of authorities of
successor pages (out-links).
Authority = scaled sum of hubbiness of
predecessor pages (in-links).
54
55. Consequences of Basic Equations
From h = λAa; a = µAT h we can
derive:
h = λµAAT h
a = λµATA a
Compute h and a by iteration,
assuming initially each page has one
unit of hubbiness and one unit of
authority.
Pick an appropriate value of λµ.
55
57. Solving H&A in Practice
Iterate as for PageRank; don’t try to
solve equations.
But keep components within bounds.
Example: scale to keep the largest
component of the vector at 1.
Trick: start with h = [1,1,…,1]; multiply
by AT to get first a; scale, then multiply
by A to get next h,…
57
58. Solving H&A – (2)
You may be tempted to compute AAT
and ATA first, then iterate these
matrices as for PageRank.
Bad, because these matrices are not
nearly as sparse as A and AT.
58
59. H&A Versus PageRank
If you talk to someone from IBM, they
may tell you “IBM invented PageRank.”
What they mean is that H&A was invented
by Jon Kleinberg when he was at IBM.
But these are not the same.
H&A does not appear to be a substitute
for PageRank.
But may be used by Ask.com.
59
60. Spam on the Web
Search has become the default
gateway to the web.
Very high premium to appear on the
first page of search results.
60
61. What is Web Spam?
Spamming = any action whose purpose
is to boost a web page’s position in
search engine results, without providing
additional value.
Spam = Web pages used for spamming.
Approximately 10-15% of Web pages
are spam.
61
62. Web-Spam Taxonomy
Boosting techniques :
Techniques for increasing the probability a
Web page will be a highly ranked answer
to a search query.
Hiding techniques :
Techniques to hide the use of boosting
from humans and Web crawlers.
62
63. Hiding techniques
Content hiding.
Use same color for text and page background.
Cloaking.
Return different page to crawlers and browsers.
Redirection.
Redirects are followed by browsers but not
crawlers.
63
64. Boosting Techniques
Term spamming :
Manipulating the text of Web pages in
order to appear relevant to queries.
• Why? You can run ads that are relevant to the
query.
Link spamming :
Creating link structures that boost
PageRank.
64
65. Term Spamming – (1)
Repetition : of one or a few specific
terms e.g., “free,” “cheap,” “Viagra.”
Dumping of a large number of
unrelated terms.
E.g., copy entire dictionaries.
65
66. Term Spamming – (2)
Weaving :
Copy legitimate pages and insert spam
terms at random positions.
Phrase Stitching :
Glue together sentences and phrases
from different sources.
• E.g., use the top-ranked pages on the topic
you want to look like.
66
67. The Google Solution to Term
Spamming
In addition to PageRank, the original
Google engine had another innovation:
it trusted what people said about you in
preference to what you said about
yourself.
Give more weight to words that appear
in or near anchor text than to words
that appear in the page itself.
67
68. The Google Solution – (2)
Today, the Google formula for
matching terms to documents involves
over 250 factors.
E.g., does the word appear in a header?
As closely guarded as the formula for
Coke.
68
69. Link Spam
Three kinds of Web pages from a
spammer’s point of view:
1. Own pages.
• Completely controlled by spammer.
2. Accessible pages.
• E.g., Web-log comment pages: spammer can
post links to his pages.
3. Inaccessible pages.
69
70. Spam Farms – (1)
Spammer’s goal:
Maximize the PageRank of target page t.
Technique:
1. Get as many links from accessible pages
as possible to target page t.
2. Construct “link farm” to get PageRank
multiplier effect.
70
71. Spam Farms – (2)
Accessible Own
1
Inaccessible
Inaccessible
2
t
M
Goal: boost PageRank of page t.
One of the most common and effective
organizations for a spam farm. 71
72. Analysis – (1)
Accessible Own
1
Inaccessible
Inaccessible 2
t
M
Suppose rank from accessible pages = x.
PageRank of target page = y. Share of
“tax”
Taxation rate = 1-β.
Rank of each “farm” page = βy/M + (1-β)/N.
From t Size of 72
Web
73. Analysis – (2)
Accessible Own Tax share
1 for t.
Inaccessible
Inaccessible 2 Very small;
t ignore.
M
y = x + βM[βy/M + (1-β)/N] + (1-β)/N
y = x + β2y + β(1-β)M/N
y = x/(1-β2) + cM/N where c = β/(1+β)
PageRank of
each “farm” page
73
74. Analysis – (3)
Accessible Own
1
Inaccessible
Inaccessible 2
t
M
y = x/(1-β2) + cM/N where c = β/(1+β).
For β = 0.85, 1/(1-β2)= 3.6.
Multiplier effect for “acquired” page rank.
By making M large, we can make y as
large as we want. 74
75. Detecting Link-Spam
Topic-specific PageRank, with a set of
“trusted” pages as the teleport set is
called TrustRank.
Spam Mass =
(PageRank – TrustRank)/PageRank.
High spam mass means most of your
PageRank comes from untrusted sources –
you may be link-spam.
75
76. Picking the Trusted Set
Two conflicting considerations:
Human has to inspect each seed page, so
seed set must be as small as possible.
Must ensure every “good page” gets
adequate TrustRank, so all good pages
should be reachable from the trusted set
by short paths.
76
77. Approaches to Picking the
Trusted Set
1. Pick the top k pages by PageRank.
It is almost impossible to get a spam
page to the very top of the PageRank
order.
2. Pick the home pages of universities.
Domains like .edu are controlled.
77